Articles | Volume 19, issue 6
https://doi.org/10.5194/gmd-19-2385-2026
https://doi.org/10.5194/gmd-19-2385-2026
Development and technical paper
 | 
25 Mar 2026
Development and technical paper |  | 25 Mar 2026

The spatio-temporal visualization tool HMMLVis in renewable energy applications

Rainer Wöß, Kateřina Hlaváčková-Schindler, Irene Schicker, Petrina Papazek, and Claudia Plant

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Cited articles

Alvarez-Castellanos, M., Ruiz, M., Serrano, A., Garcia-Algarra, J., and Moreno, Y.: Causal structure of urban air pollution: A data-driven approach using Granger causality and information theory, Environ. Pollut., 316, 120666, https://doi.org/10.1016/j.envpol.2022.120666, 2023a. a, b
Álvarez-Castellanos, R., González, J., Enguix, I., and Navarro, E.: Causality Inference for Mitigating Atmospheric Pollution in Green Ports: A Castellò Port Case Study, Eng. Proc., 56, https://doi.org/10.3390/ecsa-10-16159, 2023b. a
Behzadi, S., Hlaváčková-Schindler, K., and Plant, C.: Granger causality for heterogeneous processes, in: Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, 14–17 April 2019, Proceedings, Part III 23, 463–475, Springer, https://doi.org/10.1007/978-3-030-16148-4_36, 2019. a, b, c, d, e, f, g, h, i, j
Celik, A. and Alola, A. A.: Capital stock, energy, and innovation-related aspects as drivers of environmental quality in high-tech investing economies, Environ. Sci. Pollut. R., 30, 37004–37016, https://doi.org/10.1007/s11356-023-26327-6, 2023. a
Chan, C.-H., Juang, J.-Y., Chu, T.-H., Mao, C.-H., and Huang, S.-Y.: A Novel Evaluation of Air Pollution Impact from Stationary Emission Sources to Ambient Air Quality via Time-Series Granger Causality, in: Earth Data Analytics for Planetary Health, 33–53, Springer, https://doi.org/10.1007/978-3-031-40289-8_3, 2023. a, b
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Short summary
Our tool is an easy-to-use, interpretable causal inference software. It can be applied in any scientific discipline exploring time series. The tool uses heterogeneous Granger causality. It can be used on time-series data to infer causal relationships between multiple variables and a target time-series. The tool is demonstrated on different types of applications related to meteorological events in a renewable energy, air pollution, and postprocessing benchmark data.
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